A comparative analysis of CNN-based deep learning architectures for early diagnosis of bone cancer using CT images.

Bone cancer is a rare in which cells in the bone grow out of control, resulting in destroying the normal bone tissue. A benign type of bone cancer is harmless and does not spread to other body parts, whereas a malignant type can spread to other body parts and might be harmful. According to Cancer Research UK (2021), the survival rate for patients with bone cancer is 40% and early detection can increase the chances of survival by providing treatment at the initial stages.

Modeling the effects of age and hearing loss on concurrent vowel scores.

A difference in fundamental frequency (F0) between two vowels is an important segregation cue prior to identifying concurrent vowels. To understand the effects of this cue on identification due to age and hearing loss, Chintanpalli, Ahlstrom, and Dubno [(2016). J.

Revisiting Models of Concurrent Vowel Identification: The Critical Case of No Pitch Differences.

When presented with two vowels simultaneously, humans are often able to identify the constituent vowels. Computational models exist that simulate this ability, however they predict listener confusions poorly, particularly in the case where the two vowels have the same fundamental frequency. Presented here is a model that is uniquely able to predict the combined representation of concurrent vowels.

Modeling the level-dependent changes of concurrent vowel scores.

The difference in fundamental frequency (F0) between talkers is an important cue for speaker segregation. To understand how this cue varies across sound level, Chintanpalli, Ahlstrom, and Dubno [(2014). J.

Effects of age and hearing loss on concurrent vowel identification.

Differences in formant frequencies and fundamental frequencies (F0) are important cues for segregating and identifying two simultaneous vowels. This study assessed age- and hearing-loss-related changes in the use of these cues for recognition of one or both vowels in a pair and determined differences related to vowel identity and specific vowel pairings. Younger adults with normal hearing, older adults with normal hearing, and older adults with hearing loss listened to different-vowel and identical-vowel pairs that varied in F0 differences.

Computational model predictions of cues for concurrent vowel identification.

Although differences in fundamental frequencies (F0s) between vowels are beneficial for their segregation and identification, listeners can still segregate and identify simultaneous vowels that have identical F0s, suggesting that additional cues are contributing, including formant frequency differences. The current perception and computational modeling study was designed to assess the contribution of F0 and formant difference cues for concurrent vowel identification. Younger adults with normal hearing listened to concurrent vowels over a wide range of levels (25-85 dB SPL) for conditions in which F0 was the same or different between vowel pairs.

The use of confusion patterns to evaluate the neural basis for concurrent vowel identification.

Normal-hearing listeners take advantage of differences in fundamental frequency (F0) to segregate competing talkers. Computational modeling using an F0-based segregation algorithm and auditory-nerve temporal responses captures the gradual improvement in concurrent-vowel identification with increasing F0 difference. This result has been taken to suggest that F0-based segregation is the basis for this improvement; however, evidence suggests that other factors may also contribute.

Modeling the anti-masking effects of the olivocochlear reflex in auditory nerve responses to tones in sustained noise.

The medial olivocochlear reflex (MOCR) has been hypothesized to provide benefit for listening in noise. Strong physiological support for an anti-masking role for the MOCR has come from the observation that auditory nerve (AN) fibers exhibit reduced firing to sustained noise and increased sensitivity to tones when the MOCR is elicited. The present study extended a well-established computational model for normal-hearing and hearing-impaired AN responses to demonstrate that these anti-masking effects can be accounted for by reducing outer hair cell (OHC) gain, which is a primary effect of the MOCR.

Effect of auditory-nerve response variability on estimates of tuning curves.

Near-Poisson variability in auditory-nerve (AN) responses limits the accuracy of automated tuning-curve algorithms. Here, a typical adaptive tuning-curve algorithm was used with a physiologically realistic AN model with and without the inclusion of neural randomness. Response randomness produced variability in Q(10) estimates that was nearly as large as in AN data.